Common Variant Burden Analysis
Data loading and cohort selection
Loading
afs_orig = read.csv("data/MGRB_GnomAD_SweGen_cancer_UKBB_AFs_outerjoined_ss-auto-gwas-snps_hcr.csv.xz", stringsAsFactors = FALSE, header = TRUE)
models_orig = read.csv("data/manual_polygenic_scores.hcr_tag_rescued.csv", stringsAsFactors = FALSE, header = TRUE)
# Key allele frequencies by VID
afs_orig$vid = paste(afs_orig$chrom, afs_orig$pos, afs_orig$ref, afs_orig$alt, sep = ":")
afs_orig = afs_orig[,!(colnames(afs_orig) %in% c("chrom", "pos", "ref", "alt"))]
# Add gnomad AFs to the models for imputation of missing variants
temp.gnomad_af = (afs_orig$nAA_gnomad*2 + afs_orig$nRA_gnomad) / (2*(afs_orig$nAA_gnomad + afs_orig$nRA_gnomad + afs_orig$nRR_gnomad))
models_orig$aaf = temp.gnomad_af[match(models_orig$vid, afs_orig$vid)]
# Create a full UKBB cohort by combining the age-stratified numbers
afs_orig$nRR_ukbb = afs_orig$nRR_ukbb_0_55 + afs_orig$nRR_ukbb_55_60 + afs_orig$nRR_ukbb_60_65 + afs_orig$nRR_ukbb_65_70 + afs_orig$nRR_ukbb_70_75 + afs_orig$nRR_ukbb_75_inf
afs_orig$nRA_ukbb = afs_orig$nRA_ukbb_0_55 + afs_orig$nRA_ukbb_55_60 + afs_orig$nRA_ukbb_60_65 + afs_orig$nRA_ukbb_65_70 + afs_orig$nRA_ukbb_70_75 + afs_orig$nRA_ukbb_75_inf
afs_orig$nAA_ukbb = afs_orig$nAA_ukbb_0_55 + afs_orig$nAA_ukbb_55_60 + afs_orig$nAA_ukbb_60_65 + afs_orig$nAA_ukbb_65_70 + afs_orig$nAA_ukbb_70_75 + afs_orig$nAA_ukbb_75_inf
afs_orig$nmissing_ukbb = afs_orig$nmissing_ukbb_0_55 + afs_orig$nmissing_ukbb_55_60 + afs_orig$nmissing_ukbb_60_65 + afs_orig$nmissing_ukbb_65_70 + afs_orig$nmissing_ukbb_70_75 + afs_orig$nmissing_ukbb_75_inf
# Convert afs from wide to long format
library(reshape2)
afs_long = melt(afs_orig, id.vars = c("rsid", "negstrand", "vid"), value.name = "count")
afs_long$cohort = gsub("^n(RR|RA|AA|missing)_", "", afs_long$variable)
afs_long$variable = gsub("_.*", "", afs_long$variable)
afs = dcast(afs_long, vid + rsid + negstrand + cohort ~ variable, value.var = "count")
afs = afs[,c("vid", "rsid", "negstrand", "cohort", "nRR", "nRA", "nAA", "nmissing")]
# Exclude ASRB samples -- prelim examination suggests they are rather
# poor quality, and we are not interested in their PRS distributions
# anyway. Also exclude the various MGRB filtration options, as they
# apply only to rare variants. Exclude SweGen as we don't have a good
# HQ bed for it.
cohorts.sel = c(
"mgrborig",
"gnomad",
"ukbb", "ukbb_0_55", "ukbb_55_60", "ukbb_60_65", "ukbb_65_70", "ukbb_70_75", "ukbb_75_inf",
"cancercrc", "cancermel", "cancernms", "cancerbrca_f", "cancerpca_m",
"nocancer", "nocancer_f", "nocancer_m", "mgrborig_f", "mgrborig_m")
cohorts.main = c("mgrborig", "gnomad", "ukbb")
afs = afs[afs$cohort %in% cohorts.sel,]Model selection
Choose polygenic models with at least 10 loci, with the exception of ShortLifespan:Deelen:10.1093/hmg/ddu139 (only six loci passing filters). For cancers, choose polygenic models only for cancers with a positive control cohort. In the case of multiple models for the same disorder, choose the most recent original publication where possible (ie exclude “meta” signatures if a good original report is available).
# Excluded due to insufficient size:
# "CancerOfBladder:Fritsche:10.1016/j.ajhg.2018.04.001",
# "LymphoidLeukemiaAcute:Fritsche:10.1016/j.ajhg.2018.04.001",
# "LymphoidLeukemiaChronic:Fritsche:10.1016/j.ajhg.2018.04.001",
# "MalignantNeoplasmOfTestis:Fritsche:10.1016/j.ajhg.2018.04.001",
# "NonHodgkinsLymphoma:Fritsche:10.1016/j.ajhg.2018.04.001",
# "PancreaticCancer:Fritsche:10.1016/j.ajhg.2018.04.001",
# "APOE_rs429358:NA:NA",
# Excluded because a better alternative was available
# "CancerOfProstate:Fritsche:10.1016/j.ajhg.2018.04.001",
# "ColorectalCancer:Fritsche:10.1016/j.ajhg.2018.04.001",
# "MelanomasOfSkin:Fritsche:10.1016/j.ajhg.2018.04.001",
# "BreastCancer:Li:10.1038/gim.2016.43",
# "BreastCancerFemale:Fritsche:10.1016/j.ajhg.2018.04.001",
# Excluded because of issues with population-specific alleles between UK and European popns
# "BasalCellCarcinoma:Chahal:10.1038/ncomms12510",
# "BasalCellCarcinoma:Fritsche:10.1016/j.ajhg.2018.04.001",
models.sel = c(
"AF:Lubitz:10.1161/CIRCULATIONAHA.116.024143",
"DiastolicBP:Warren:10.1038/ng.3768",
"EOCAD:Theriault:10.1161/circgen.117.001849",
"PulsePressure:Warren:10.1038/ng.3768",
"SystolicBP:Warren:10.1038/ng.3768",
"AlzheimersDisease:Lambert:10.1038/ng.2802",
"ShortLifespan:Deelen:10.1093/hmg/ddu139",
"Height:Wood:10.1038/ng.3097",
"BreastCancer:Michailidou:10.1038/nature24284",
"ColorectalCancer:Schumacher:10.1038/ncomms8138",
"Melanoma:Law:10.1038/ng.3373",
"ProstateCancer:Hoffmann:10.1158/2159-8290.CD-15-0315"
)
models = models_orig[models_orig$id %in% models.sel,]Variant filtering and imputation
# Drop variants with low genotyping rate in any cohort in which that
# variant was detected. Use the threshold of 97% genotyping rate
library(plyr)
temp.lowgt = ddply(afs[!is.na(afs$nRR),], .(cohort), function(d) mean(d$nmissing / (d$nRR + d$nRA + d$nAA + d$nmissing) >= 0.03))
temp.lowgt## cohort V1
## 1 cancerbrca_f 1.637465e-04
## 2 cancercrc 1.228099e-04
## 3 cancermel 1.228099e-04
## 4 cancernms 8.187326e-05
## 5 cancerpca_m 4.093663e-05
## 6 gnomad 1.598146e-03
## 7 mgrborig 8.074609e-05
## 8 mgrborig_f 8.074609e-05
## 9 mgrborig_m 8.074609e-05
## 10 nocancer 8.187326e-05
## 11 nocancer_f 4.093663e-05
## 12 nocancer_m 1.228099e-04
## 13 ukbb 5.066030e-02
## 14 ukbb_0_55 5.120115e-02
## 15 ukbb_55_60 5.097580e-02
## 16 ukbb_60_65 5.097580e-02
## 17 ukbb_65_70 5.057015e-02
## 18 ukbb_70_75 5.038987e-02
## 19 ukbb_75_inf 4.989408e-02# The UKBB samples have rather a lot of dropouts here: ~ 5% of loci
# have a gt rate under 97%.
temp.gt_rate = 1 - daply(afs[!is.na(afs$nRR),], .(vid), function(d) max(d$nmissing / (d$nRR + d$nRA + d$nAA + d$nmissing)))
mean(temp.gt_rate < 0.97)## [1] 0.04893795afs = afs[afs$vid %in% names(temp.gt_rate)[temp.gt_rate >= 0.97],]
mean(models$vid %in% names(temp.gt_rate)[temp.gt_rate >= 0.97])## [1] 0.9484241# ~5.2% of model loci lost by this filter
models = models[models$vid %in% names(temp.gt_rate)[temp.gt_rate >= 0.97],]
# Create a set of AFs for variants that have VCF entries in every cohort.
# Note that given the relatively small size of some cohorts, this tends to
# preferentially exclude rare variants from consideration, and will probably
# attenuate the GRS differences.
afs.nmissing_per_cohort = tapply(is.na(afs$nRR), afs$vid, sum)
afs.nomissing = afs[!(afs$vid %in% names(afs.nmissing_per_cohort[afs.nmissing_per_cohort > 0])),]
nrow(afs.nomissing) / nrow(afs)## [1] 0.8663734mean(models$vid %in% afs.nomissing$vid)## [1] 0.6628399Cohort AF comparisons
Note that we examine only loci in the PRSes.
Overall distribution
temp.model_vids = intersect(afs.nomissing$vid, models$vid)
temp.afs = afs.nomissing[afs.nomissing$vid %in% temp.model_vids,]
temp.afs$fA = (temp.afs$nAA*2 + temp.afs$nRA) / (2*(temp.afs$nAA + temp.afs$nRA + temp.afs$nRR))
temp.afs = acast(temp.afs, vid ~ cohort, value.var = "fA", fill = NA)
pairs(temp.afs[,cohorts.main], pch = ".")
temp.overall_cohort.pvals = sapply(cohorts.main[1:(length(cohorts.main)-1)], function(cohort_1) {
cohort_1_idx = which(cohorts.main == cohort_1)
afs_1 = temp.afs[,cohort_1]
sapply(cohorts.main[(cohort_1_idx+1):length(cohorts.main)], function(cohort_2) {
afs_2 = temp.afs[,cohort_2]
test = wilcox.test(afs_1 - afs_2)
test$p.value
})
})
temp.overall_cohort.pvals## $mgrborig
## gnomad ukbb
## 0.69792760 0.01866417
##
## $gnomad
## ukbb
## 0.3782686p.adjust(unlist(temp.overall_cohort.pvals), "holm")## mgrborig.gnomad mgrborig.ukbb gnomad.ukbb
## 0.75653715 0.05599252 0.75653715Individual loci
g.test = function(tbl)
{
expected = outer(rowSums(tbl), colSums(tbl)) / sum(tbl)
logoe = log(tbl/expected)
logoe[tbl == 0] = 0
stat = 2*sum(tbl*logoe)
pchisq(stat, prod(dim(tbl)-1), lower.tail = FALSE)
}
temp.locus_cohort.pvals = ddply(afs.nomissing[afs.nomissing$vid %in% temp.model_vids & afs.nomissing$cohort %in% cohorts.main,], .(vid), function(d) {
nR = d$nRR*2 + d$nRA
nA = d$nAA*2 + d$nRA
g.test(cbind(nR, nA))
# g.test(as.matrix(d[,c("nRR", "nRA", "nAA")]))
})
colnames(temp.locus_cohort.pvals)[2] = "p.value"
temp.locus_cohort.pvals$p.value.mtc = p.adjust(temp.locus_cohort.pvals$p.value, "BH")
# temp.locus_cohort.pvals$p.value.mtc is now calibrated for average false rejection rate
# (ie calling a SNP population-associated when it in fact isn't).
mean(temp.locus_cohort.pvals$p.value.mtc < 0.01)## [1] 0.4721461temp.locus_cohort.maxdeltaaf = ddply(afs.nomissing[afs.nomissing$vid %in% temp.model_vids & afs.nomissing$cohort %in% cohorts.main,], .(vid), function(d) {
nR = d$nRR*2 + d$nRA
nA = d$nAA*2 + d$nRA
AAF = nA/(nA+nR)
max(AAF) - min(AAF)
})
colnames(temp.locus_cohort.maxdeltaaf)[2] = "maxdeltaaaf"
temp.locus_cohort.maxdeltaaf[order(temp.locus_cohort.maxdeltaaf$maxdeltaaaf),]## vid maxdeltaaaf
## 629 20:41851935:G:A 4.804901e-05
## 175 11:244552:A:G 5.472387e-04
## 680 3:163838015:A:C 5.854864e-04
## 832 5:176517326:T:C 6.520533e-04
## 849 5:58337481:T:G 6.896540e-04
## 576 2:232268312:T:C 8.648204e-04
## 64 1:242034263:A:G 8.926576e-04
## 557 2:19942473:G:A 8.944422e-04
## 783 4:61995613:A:G 9.804085e-04
## 343 15:32993111:C:T 1.013646e-03
## 125 10:12943973:C:T 1.036166e-03
## 428 17:43216281:C:T 1.120399e-03
## 923 6:43711981:T:C 1.242711e-03
## 87 1:65010606:T:G 1.410640e-03
## 1076 9:22062134:G:T 1.487932e-03
## 943 6:81792063:G:T 1.508864e-03
## 368 15:72842705:G:A 1.511067e-03
## 716 3:62133492:G:A 1.517305e-03
## 754 4:147993702:A:G 1.550150e-03
## 49 1:215046892:G:A 1.631525e-03
## 45 1:203766331:A:G 1.651584e-03
## 775 4:39503196:A:G 1.678110e-03
## 799 5:108625324:C:A 1.746323e-03
## 34 1:17308254:T:C 1.809376e-03
## 462 18:19450303:A:G 1.867013e-03
## 460 17:800593:T:C 1.957503e-03
## 661 21:45867411:G:A 1.968592e-03
## 290 13:55934157:A:G 1.984521e-03
## 1095 9:99203606:T:C 2.015219e-03
## 543 2:179786068:T:C 2.214285e-03
## 221 12:125441159:T:C 2.246113e-03
## 364 15:67455630:C:T 2.312045e-03
## 444 17:64783539:C:T 2.316572e-03
## 1064 9:118826916:G:A 2.361107e-03
## 383 15:98615560:C:T 2.370182e-03
## 698 3:190815978:A:G 2.376253e-03
## 17 1:151259043:C:T 2.419978e-03
## 950 7:107259721:T:C 2.424953e-03
## 918 6:36659932:C:T 2.450101e-03
## 670 3:136107549:G:A 2.505036e-03
## 13 1:14105298:G:A 2.546910e-03
## 871 6:118569679:T:G 2.552647e-03
## 993 8:110115372:C:T 2.598390e-03
## 158 11:11563879:C:T 2.615930e-03
## 300 13:80618435:A:G 2.616690e-03
## 965 7:148629759:C:T 2.621543e-03
## 701 3:27416013:C:T 2.622335e-03
## 548 2:191227755:A:G 2.638522e-03
## 477 18:57323149:C:A 2.650551e-03
## 1005 8:128106880:A:C 2.808417e-03
## 36 1:177279412:G:A 2.814433e-03
## 791 4:82204091:A:G 2.816117e-03
## 936 6:75452066:T:C 2.865091e-03
## 502 19:42683964:C:T 2.865981e-03
## 801 5:113748571:C:T 2.886389e-03
## 633 20:50141264:T:C 2.900362e-03
## 883 6:149608874:G:A 2.955662e-03
## 995 8:115698881:G:A 2.962404e-03
## 482 18:77222862:T:G 2.971526e-03
## 332 14:90636206:G:A 3.008568e-03
## 572 2:219903723:C:T 3.051369e-03
## 650 21:27208935:G:T 3.062235e-03
## 827 5:171285632:C:T 3.075935e-03
## 606 2:56113538:G:A 3.098396e-03
## 812 5:141681788:G:A 3.104595e-03
## 1067 9:127900996:T:C 3.347011e-03
## 687 3:178467852:A:G 3.415386e-03
## 618 20:19465907:G:A 3.418120e-03
## 691 3:185313855:A:G 3.418663e-03
## 153 10:97805074:G:A 3.442529e-03
## 942 6:81038921:T:G 3.447571e-03
## 994 8:110581794:A:G 3.470129e-03
## 180 11:45643843:C:T 3.483223e-03
## 466 18:29088958:C:T 3.498834e-03
## 865 6:109285189:A:G 3.553083e-03
## 390 16:2345388:T:C 3.579194e-03
## 58 1:227191011:A:G 3.623518e-03
## 777 4:42308930:C:T 3.649195e-03
## 600 2:44392271:C:A 3.688753e-03
## 939 6:7709052:G:T 3.701351e-03
## 723 3:89530057:C:A 3.732115e-03
## 739 4:111765495:C:T 3.739535e-03
## 1000 8:123980551:C:T 3.752414e-03
## 1038 8:57095808:T:C 3.768970e-03
## 808 5:134499092:C:A 3.775173e-03
## 790 4:82184049:C:T 3.790315e-03
## 342 15:30167418:G:A 3.798314e-03
## 8 1:11852516:C:T 3.841580e-03
## 674 3:141163045:T:C 3.905120e-03
## 686 3:178343683:C:T 3.911620e-03
## 362 15:66997087:A:G 3.913701e-03
## 96 1:89360520:T:G 3.941459e-03
## 1085 9:92498089:C:T 3.992649e-03
## 437 17:56431549:A:G 4.036653e-03
## 744 4:119121695:C:T 4.041940e-03
## 480 18:74983055:A:G 4.072636e-03
## 164 11:128478885:C:A 4.108551e-03
## 82 1:54119578:G:A 4.159979e-03
## 1037 8:56998480:G:A 4.187366e-03
## 1080 9:6365683:A:C 4.206827e-03
## 433 17:47944836:G:A 4.216069e-03
## 571 2:219154781:G:A 4.227556e-03
## 1066 9:126094366:T:C 4.254321e-03
## 438 17:59483766:C:T 4.254752e-03
## 647 21:16520832:G:A 4.264179e-03
## 212 12:114804580:A:C 4.266542e-03
## 268 13:111100780:C:T 4.279067e-03
## 578 2:232796610:T:C 4.294600e-03
## 315 14:53391680:A:G 4.313766e-03
## 307 14:100599519:T:C 4.321819e-03
## 703 3:38036914:C:T 4.336252e-03
## 848 5:56254485:G:T 4.379536e-03
## 919 6:39134099:T:C 4.392131e-03
## 699 3:191111160:A:G 4.457648e-03
## 73 1:32371442:C:T 4.488190e-03
## 901 6:17589375:T:C 4.528051e-03
## 40 1:195866450:T:G 4.531433e-03
## 440 17:62161933:A:G 4.543676e-03
## 176 11:2810731:C:T 4.564121e-03
## 327 14:70609793:G:A 4.566616e-03
## 309 14:24830850:T:C 4.616386e-03
## 273 13:22453087:T:C 4.668375e-03
## 467 18:29363000:G:A 4.700294e-03
## 337 14:93784292:A:C 4.705838e-03
## 930 6:51450906:C:T 4.742416e-03
## 114 10:114773927:A:G 4.743066e-03
## 831 5:175947118:G:T 4.774634e-03
## 539 2:177557020:A:G 4.794079e-03
## 299 13:78474468:A:C 4.812120e-03
## 43 1:20030836:G:A 4.816278e-03
## 174 11:23197362:G:A 4.825182e-03
## 83 1:56965664:C:T 4.866564e-03
## 510 19:56323209:T:C 4.882986e-03
## 395 16:52599188:C:T 4.899116e-03
## 135 10:44777188:C:A 4.900737e-03
## 839 5:32821168:A:G 4.908845e-03
## 1010 8:129194641:C:T 4.921311e-03
## 134 10:44480811:T:G 4.943897e-03
## 267 13:111049623:T:C 4.952131e-03
## 170 11:2022804:A:G 4.978664e-03
## 414 17:1353920:T:G 5.093687e-03
## 733 4:109408608:G:A 5.102530e-03
## 574 2:221397008:T:C 5.228419e-03
## 426 17:40565926:T:G 5.228988e-03
## 249 12:6385727:C:T 5.238769e-03
## 722 3:74522679:G:A 5.286244e-03
## 523 2:142077308:T:C 5.309499e-03
## 347 15:39531658:A:G 5.312263e-03
## 351 15:51269629:A:G 5.343431e-03
## 384 15:99219598:C:T 5.350541e-03
## 348 15:39597034:T:C 5.358179e-03
## 264 12:96027759:A:G 5.362742e-03
## 999 8:117630683:A:C 5.369172e-03
## 810 5:137044526:T:C 5.376612e-03
## 1059 9:114850255:A:C 5.388091e-03
## 770 4:29571133:C:T 5.413917e-03
## 376 15:81836638:G:A 5.443466e-03
## 90 1:77559860:G:A 5.447211e-03
## 755 4:148400819:C:A 5.449392e-03
## 768 4:184529029:T:G 5.475820e-03
## 132 10:35150975:A:G 5.499058e-03
## 1073 9:139111870:G:T 5.525798e-03
## 152 10:96632253:T:C 5.534003e-03
## 542 2:179349227:T:G 5.541236e-03
## 142 10:75683793:G:A 5.549552e-03
## 835 5:31540303:A:G 5.551595e-03
## 880 6:13722523:G:A 5.632665e-03
## 103 10:102604514:G:A 5.670602e-03
## 341 15:101762539:C:T 5.709396e-03
## 50 1:21584585:A:G 5.722582e-03
## 141 10:75416789:A:G 5.731772e-03
## 210 12:114556438:C:T 5.756619e-03
## 814 5:142928348:C:A 5.757560e-03
## 1069 9:133464084:A:G 5.818366e-03
## 676 3:14928077:G:T 5.820147e-03
## 945 6:82442022:G:A 5.842640e-03
## 429 17:46096276:C:T 5.845469e-03
## 547 2:190404135:C:T 5.887918e-03
## 457 17:7571752:T:G 5.938427e-03
## 764 4:175846426:C:A 5.946180e-03
## 913 6:34195011:G:T 5.966954e-03
## 561 2:207948768:C:T 5.969859e-03
## 261 12:93919840:C:T 6.005322e-03
## 350 15:48914926:A:G 6.033136e-03
## 653 21:32487917:C:T 6.037518e-03
## 77 1:41101572:A:G 6.077944e-03
## 521 2:134434824:T:C 6.094110e-03
## 208 12:104344836:A:G 6.112237e-03
## 644 20:6572014:A:C 6.117750e-03
## 663 3:111560228:G:A 6.119474e-03
## 329 14:74990746:A:G 6.133487e-03
## 333 14:91841069:A:G 6.134627e-03
## 756 4:148763687:C:T 6.140946e-03
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## 728 4:106061534:C:A 4.717647e-02
## 734 4:111586468:A:G 4.918761e-02
## 224 12:14520701:A:G 4.978408e-02
## 806 5:131585958:A:G 5.031346e-02
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## 312 14:32981484:G:A 5.612800e-02
## 736 4:111706275:A:G 5.732161e-02hist(temp.locus_cohort.maxdeltaaf$maxdeltaaaf)
# On the basis of this histogram, set a max delta aaf threshold of
# 4%.
mean(temp.locus_cohort.maxdeltaaf$maxdeltaaaf < 0.04)## [1] 0.9881279temp.sel_loci = temp.locus_cohort.maxdeltaaf$vid[temp.locus_cohort.maxdeltaaf$maxdeltaaaf < 0.04]
afs = afs[afs$vid %in% temp.sel_loci,]
models = models[models$vid %in% temp.sel_loci,]Compare PRS distributions between cohorts
PRS calculation
scores = daply(expand.grid(cohort = cohorts.sel, model = models.sel), .(model, cohort), function(cm)
cohort_mean_score(afs.nomissing[afs.nomissing$cohort == cm$cohort,], models[models$id == cm$model,]), .progress = "text")
set.seed(314159)
boot_scores = daply(expand.grid(cohort = cohorts.sel, model = models.sel), .(model, cohort), function(cm)
cohort_mean_score_boot(afs.nomissing[afs.nomissing$cohort == cm$cohort,], models[models$id == cm$model,], B = 5000, drawmissing = TRUE), .progress = "text")# Calculate all approximate tests, but obviously we will discard most
# of the outputs.
set.seed(314159)
boot_scores_tests = boot_tests(boot_scores, B = 100000)saveRDS(list(
scores = scores,
boot = boot_scores,
tests = boot_scores_tests), file = "common_variants.rds")PRS distributions
library(gplots)##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowesslibrary(viridis)## Loading required package: viridisLiteheatmap.2(scores, trace = "none", scale = "row", dendrogram = "none", Colv = FALSE, Rowv = FALSE, col = viridis(100), margin = c(7, 25))
library(ggplot2)
ggplot(adply(boot_scores, c(1,2), function(x) data.frame(boot_score = x)), aes(x = boot_score, col = cohort)) + stat_ecdf() + facet_wrap(~ model, scales = "free") + theme_bw()